Do circadian factors improve mortality risk prediction? A dual-cohort analysis of metabolic and circadian syndromes

IF 7.4 3区医学 Q1 ENDOCRINOLOGY & METABOLISM

Diabetes research and clinical practice Pub Date : 2025-09-22 DOI:10.1016/j.diabres.2025.112919

Da Pan , Yuanyuan Wang , Chen Zhang , Yifei Lu , Shiyu Yin , Pei Wang , Jiayue Xia , Junhui Yu , Han Gao , Guiju Sun , Dengfeng Xu

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Abstract

Aims

This study prospectively evaluated the associations between metabolic syndrome (MetS), circadian syndrome (CircS) and mortality from major diseases.

Methods

We conducted prospective analyses using data from two large cohorts: NHANES (n = 14,727 for MetS; 13,799 for CircS, aged ≥ 20 years) and the UK Biobank (n = 404,028 for MetS; 376,236 for CircS, aged ≥ 40 years). We assessed associations between MetS, CircS, and all-cause, cardiovascular, and cancer mortality, using Cox proportional hazards models. Model performance was compared using C-index, Brier Score, AIC, and BIC.

Results

In NHANES, the prevalences of MetS and CircS were 41.1 % and 31.7 %, respectively, while in UKB, they were 29.9 % and 17.6 %, respectively. In fully adjusted models, MetS was significantly associated with a 21 % increase in all-cause mortality, and CircS was significantly associated with a 34 % increase in all-cause mortality and a 65 % increase in heart diseases mortality in the NHANES cohort; both MetS and CircS were significantly associated with increased risks of mortality from heart diseases (MetS: HR = 1.68; 95 % CI, 1.58–1.79; CircS: HR = 1.78; 95 % CI, 1.66–1.90), cancers (MetS: HR = 1.19; 95 % CI, 1.15–1.24; CircS: HR = 1.24; 95 % CI, 1.19–1.29), CVD (MetS: HR = 1.56; 95 % CI, 1.48–1.64; CircS: HR = 1.66; 95 % CI, 1.56–1.75), ischemic heart disease (MetS: HR = 1.81; 95 % CI, 1.68–1.96; CircS: HR = 1.93; 95 % CI, 1.79–2.09), stroke (MetS: HR = 1.36; 95 % CI, 1.21–1.53; CircS: HR = 1.42; 95 % CI, 1.24–1.61), and all-cause mortality (MetS: HR = 1.30; 95 % CI, 1.26–1.33; CircS: HR = 1.42; 95 % CI, 1.38–1.46) in the UKB cohort. CircS showed stronger associations with all mortality outcomes across both cohorts. Subgroup analyses revealed stronger associations in middle-aged adults, but weaker associations in those ≥ 70 years. CircS-based models consistently outperformed MetS-based models in terms of AIC and BIC.

Conclusions

CircS may provide a more comprehensive and interpretable framework for mortality risk prediction than MetS. Its potential incorporation into clinical and public health practice warrants further investigation.

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昼夜节律因素能改善死亡风险预测吗？代谢和昼夜综合征的双队列分析。

目的：本研究前瞻性评估代谢综合征（MetS）、昼夜节律综合征（CircS）与主要疾病死亡率之间的关系。方法:我们进行了前瞻性分析使用数据从两大群体:NHANES (n为大都会 = 14727;13799情况,年龄 ≥  20年)和英国生物库(n为大都会 = 404028;376236情况,年龄 ≥  40年)。我们使用Cox比例风险模型评估MetS、CircS与全因死亡率、心血管死亡率和癌症死亡率之间的关系。采用c指数、Brier评分、AIC和BIC对模型性能进行比较。结果：在NHANES中，met和CircS的患病率分别为41.1% %和31.7% %，而在UKB中，met和CircS的患病率分别为29.9% %和17.6% %。在完全调整的模型中，在NHANES队列中，MetS与全因死亡率增加21% %显著相关，CircS与全因死亡率增加34% %和心脏病死亡率增加65% %显著相关；大都会和情况都与增加心脏病死亡率的风险显著相关(大都会:HR = 1.68; 95 % CI, 1.58 - -1.79;情况:HR = 1.78; 95年 % CI, 1.66 - -1.90),癌症(大都会:HR = 1.19; 95 % CI, 1.15 - -1.24;情况:HR = 1.24; 95年 % CI, 1.19 - -1.29),心血管疾病(大都会:HR = 1.56; 95 % CI, 1.48 - -1.64;情况:HR = 1.66; 95年 % CI, 1.56 - -1.75),缺血性心脏病(大都会:HR = 1.81; 95年 % CI, 1.68 - -1.96;情况:HR = 1.93; 95年 % CI, 1.79 - -2.09),中风(大都会:HR = 1.36; 95年 % CI, 1.21 - -1.53;CircS: HR = 1.42；95 % CI, 1.24-1.61)和全因死亡率（met: HR = 1.30; 95 % CI, 1.26-1.33; CircS: HR = 1.42; 95 % CI, 1.38-1.46）。CircS与两组人群的所有死亡率结果均有较强的相关性。亚组分析显示中年人的相关性较强，但在 ≥ 70 岁的人群中相关性较弱。基于circs的模型在AIC和BIC方面始终优于基于met的模型。结论：CircS可能比MetS提供更全面和可解释的死亡率风险预测框架。将其纳入临床和公共卫生实践的可能性值得进一步调查。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diabetes research and clinical practice 医学-内分泌学与代谢

CiteScore

10.30

自引率

3.90%

发文量

862

审稿时长

32 days

期刊介绍： Diabetes Research and Clinical Practice is an international journal for health-care providers and clinically oriented researchers that publishes high-quality original research articles and expert reviews in diabetes and related areas. The role of the journal is to provide a venue for dissemination of knowledge and discussion of topics related to diabetes clinical research and patient care. Topics of focus include translational science, genetics, immunology, nutrition, psychosocial research, epidemiology, prevention, socio-economic research, complications, new treatments, technologies and therapy.